A Longitudinal Study of Language Trajectories and Treatment Outcomes of Early Intensive Behavioral Intervention for Autism

Frazier, Thomas; Klingemier, Eric W.; Anderson, Christa J.; Gengoux, Grace; Youngstrom, Eric A.; Hardan, Antonio Y.

doi:10.1007/s10803-021-04900-5

Cited by 33 publications

(32 citation statements)

References 62 publications

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“…The treatment of ASD and GDD/ID are not all the same, current evidence supports that ASD should be primarily targeted at social communication skills complemented by management of abnormal behavior, but children with GDD may obtain greater benefit from structured daily routines, cognitive behavioral therapy and so on ( 11 , 12 ). Early diagnosis and targeted treatment are of great significance to improve the prognosis ( 13 – 15 ). A small amount of studies have investigated ASD in the ID population, and found that the comorbidity rate of ASD in the ID population was 4.2–32.9%, and ID children with comorbid ASD had more severe intellectual disability than children with ID alone ( 16 – 18 ).…”

Section: Introductionmentioning

confidence: 99%

Prevalence and Developmental Profiles of Autism Spectrum Disorders in Children With Global Developmental Delay

et al. 2022

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BackgroundPrevious studies have mostly explored the comorbidities of Global developmental delay (GDD) in children with Autism Spectrum Disorders (ASD) from the perspective of ASD. The study focus on the perspective of GDD to investigate the prevalence and developmental profiles of ASD in GDD and to explore the correlation between the developmental level and symptoms of autism.MethodsClinical data of 521 children with GDD aged from 24 to 60 months were retrospectively analyzed. Analyses were performed first for the whole sample and then subdivided into two subgroups (GDD+ASD−, GDD+ASD+) according to whether had ASD. Symptoms of autism were evaluated by the Autism Behavior Checklist and the Childhood Autism Rating Scale. The Chinese version of the Gesell Developmental Schedules was used to evaluate the level of children's mental development.ResultThe prevalence of ASD in children with GDD was 62.3%. The total average developmental quotient (DQ) of GDD was mildly deficient and was negatively correlated with symptoms of autism (p < 0.05); language ability was severe and extremely severe deficient (P < 0.05). GDD+ASD− group and GDD+ASD+ group have some common points as well as differences in the developmental features. The language delay of children in both subgroups was the most obviously defected, thereafter followed by the item of personal social activity. In the GDD+ASD+ group, the DQ of gross motor skills>fine motor skills>adaptability (p < 0.05). There were no significant differences among the DQ of gross motor skills, fine motor skills and adaptability in GDD+ASD− group (p > 0.05). The GDD+ASD−group had better adaptability, fine motor skills, language ability, personal social activity than that of the GDD+ASD+ group, but the gross motor skills in GDD+ASD− group were worse (p < 0.05).ConclusionGDD children have a high proportion of comorbid ASD, and GDD children with poorer developmental levels are more likely to have ASD symptoms. Development profiles in both GDD+ASD− children and GDD+ASD+ children have common features but there are also differences. GDD+ASD+ group is worse than GDD+ASD− group in terms of the overall development level.

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Section: Introductionmentioning

confidence: 99%

Prevalence and Developmental Profiles of Autism Spectrum Disorders in Children With Global Developmental Delay

et al. 2022

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“…As we continue to advance our work on describing treatmentrelated change over time, it is also important to identify predictors of "more responsive" trajectories (e.g., higher rates of improvement, longer maintenance of treatment effects). As reported in the intervention studies mentioned above (19,20), some child demographics (e.g., age, sex) and characteristics at baseline (e.g., IQ, level of symptoms) were associated with different trajectories during and/or after the intervention. What remains unclear, however, is the dynamic processes between predictors and treatment outcomes underlying these variable trajectories.…”

Section: Modeling Dynamic Prediction Of Treatment Responsementioning

confidence: 77%

“…In an RCT study (10), variable trajectories of joint attention behaviors were observed among a group of preschool-aged children diagnosed with Autism over the course of social communication intervention and 5year follow-up, where the change patterns were associated with treatment assignment and diagnostic status at the exit. A recent observational study (19) reported an overall increase across diverse language trajectories between the entry and exit of an early intensive behavioral intervention (EIBI) program among preschool-aged children with Autism, with steeper improvements predicted by younger age, higher cognitive abilities, and lower symptom severity at baseline. Another observational study (20) examined the growth curve of autistic children's developmental outcomes across several time-points during applied behavior analysis (ABA) intervention and found that symptom severity, primary language spoken at home, and child's sex, but not treatment intensity and age of entry, were significant predictors of growth rates in certain outcomes during the intervention.…”

Section: Capturing Individual-level Variability In Treatment Outcomesmentioning

confidence: 99%

Rethinking Autism Intervention Science: A Dynamic Perspective

2022

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Recent advances in longitudinal methodologies for observational studies have contributed to a better understanding of Autism as a neurodevelopmental condition characterized by within-person and between-person variability over time across behavioral domains. However, this finer-grained approach to the study of developmental variability has yet to be applied to Autism intervention science. The widely adopted experimental designs in the field—randomized control trials and quasi-experimental designs—hold value for inferring treatment effects; at the same time, they are limited in elucidating what works for whom, why, and when, given the idiosyncrasies of neurodevelopmental disorders where predictors and outcomes are often dynamic in nature. This perspective paper aims to serve as a primer for Autism intervention scientists to rethink the way we approach predictors of treatment response and treatment-related change using a dynamic lens. We discuss several empirical gaps, and potential methodological challenges and opportunities pertaining to: (1) capturing finer-grained treatment effects in specific behavioral domains as indexed by micro-level within-person changes during and beyond intervention; and (2) examining and modeling dynamic prediction of treatment response. Addressing these issues can contribute to enhanced study designs and methodologies that generate evidence to inform the development of more personalized interventions and stepped care approaches for individuals on the heterogeneous spectrum of Autism with changing needs across development.

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“…This can be achieved by applying cluster analyses (CA), a strategy that has already been applied to samples of preschoolers with ASD to investigate ASD heterogeneity (for a review see 26). Recently Frazier et al (27) used CA to identify subgroups of language development during an applied behavioral analysis (ABA) intervention. However, no study to date has applied CA on a sample of preschoolers who received early intensive intervention to define data-driven subgroups based on developmental or symptom levels at intervention start and rates of change over time.…”

Section: Introductionmentioning

confidence: 99%

Distinct patterns of cognitive outcome in young children with autism spectrum disorder receiving the Early Start Denver Model

Godel

Robain

Kojovic

et al. 2021

Preprint

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Evidence-based early intervention approaches have been shown to significantly improve many areas of development in young children with autism. Despite having an overall positive impact for most children, there is high inter-individual heterogeneity of response to treatment, and it is currently not possible to predict which child will benefit from which specific intervention. In this study, we analyze developmental trajectories of young children with autism who received two-years of Early Start Denver Model intervention in Geneva, Switzerland (n=47). Using cluster analyses, we distinguish between 3 subgroups based on cognitive level at baseline and rates of change over time. The first subgroup of children had cognitive scores at baseline only slightly below what would be expected for their age and were found to have nearly no cognitive delay by the end of treatment (High at Baseline: HB). The children in the two other subgroups all presented with severe cognitive delay at baseline, with the second group′s cognitive scores improving significantly in the first year of intervention, along with increased communication and daily living skills (High Responders: HR). The third subgroup showed little to no cognitive level change over the course of treatment (Minimal Responders: MR). Furthermore, the rates of change in cognition and adaptative behavior in the HR subgroup were already significantly higher compared to the MR subgroup within the first 6 months of intervention. A rapid and early response to intervention might thus represent a predictor of optimal outcome. Inversely, slower progress by the sixth month of intervention may predict that the child will have a slower response to the treatment overall and may need alternative supports to reach their learning objectives quickly.

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A Longitudinal Study of Language Trajectories and Treatment Outcomes of Early Intensive Behavioral Intervention for Autism

Cited by 33 publications

References 62 publications

Prevalence and Developmental Profiles of Autism Spectrum Disorders in Children With Global Developmental Delay

Prevalence and Developmental Profiles of Autism Spectrum Disorders in Children With Global Developmental Delay

Rethinking Autism Intervention Science: A Dynamic Perspective

Distinct patterns of cognitive outcome in young children with autism spectrum disorder receiving the Early Start Denver Model

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